Who is Afraid of Model Pluralism?

Taking scientific practice as its starting point, this book charts the complex territory of models used in science. It examines what scientific models are and what their function is. Reliance on models is pervasive in science, and scientists often need to construct models in order to explain or predict anything of interest at all. The diversity of kinds of models one finds in science – ranging from toy models and scale models to theoretical and mathematical models – has attracted attention not only from scientists, but also from philosophers, sociologists, and historians of science. This has given rise to a wide variety of case studies that look at the different uses to which models have been put in specific scientific contexts. By exploring current debates on the use and building of models via cutting-edge examples drawn from physics and biology, the book provides broad insight into the methodology of modelling in the natural sciences. It pairs specific arguments with introductory material relating to the ontology and the function of models, and provides some historical context to the debates as well as a sketch of general positions in the philosophy of scientific models in the process.

Philosophy of Social Science

This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling-practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: (i) any successful analysis of models must target sets of models, their multiplicity of functions within science, and their scientific context and history and (ii) for almost any aspect x of phenomenon y, scientists require multiple models to achieve scientific goal z.

TEORIE VĚDY / THEORY OF SCIENCE, 2017

Review: Emiliano Ippoliti, Fabio Sterpetti and Thomas Nickles, eds. Models and Inferences in Science. Cham: Springer, 2016, 256 pages.

Models are ubiquitous across disciplines, but the model as a means of conveying knowledge and abstracting reality is little studied. Scholars in different disciplines may use a variety of divergent types of models and each understands his or her world through a prismatic view provided by the specialized model used. If each field focuses only on its own variety of model and each model has shortcomings, we’re still missing the big picture. The authors propose a taxonomy that may be used to classify the universe of models as used in numerous scientific and non-scientific disciplines. Such a framework can enable scholars to better communicate with their counterparts in other disciplines and to adapt the models of other disciplines to their own, thereby gaining a better understanding of their own specialized areas of study.

In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the philosophical debate on modeling. Specifically, I review my empirical investigations into the issues of model de-idealization, model justification and performativity.

2016

This paper focuses on scientists ’ views of scientific models and their use in authentic practice. Participants were 24 scientists, averaging 25 years research experience, representing four discipline areas. Views of scientific models were assessed through an open-ended questionnaire (VNOS-Sci) and interviews. The scientists described models relative to their research in a variety of ways, from model development to model use through testing of predictions. Model development and model use were described as distinct practices. Those who emphasized model use had a greater tendency to emphasize prediction in scientific research. The analysis revealed multiple descriptions of the purpose of models in authentic practice. The majority of the scientists reported that models explain or organize observations/predict/test. Other descriptions included: models provide understanding of system/complexity made simple/abstract made visual, models are mathematical representations, models are represen...

Online Submission, 2005

This paper focuses on scientists' views of scientific models and their use in authentic practice. Participants were 24 scientists, averaging 25 years research experience, representing four discipline areas. Views of scientific models were assessed through an open-ended questionnaire (VNOS-Sci) and interviews. The scientists described models relative to their research in a variety of ways, from model development to model use through testing of predictions. Model development and model use were described as distinct practices. Those who emphasized model use had a greater tendency to emphasize prediction in scientific research. The analysis revealed multiple descriptions of the purpose of models in authentic practice. The majority of the scientists reported that models explain or organize observations/predict/test. Other descriptions included: models provide understanding of system/complexity made simple/abstract made visual, models are mathematical representations, models are representations of physical systems, and models provide a directing framework for research. Variations in frequency of these descriptions amongst the scientists are discussed. Several responses demonstrated a connection between views of models and views of certainty and hierarchy of scientific knowledge. Results also suggest scientists' descriptions of model purpose and use may differ based on scientific discipline and investigative approach utilized in scientific research. Paper presented as part of the symposium, "Learning about models and modeling in science: International views of research issues" at the annual meeting of the

… on the Simulation and Synthesis of …, 2000

We review and critique an range of perspectives on the scientific role of individual-based evolutionary simulation models as they are used within artificial life. We find that such models have the potential to enrich existing modelling enterprises through their strength in modelling systems of interacting entities. Furthermore, simulation techniques promise to provide theoreticians in various fields with entirely new conceptual, as well as methodological, approaches. However, the precise manner in which simulations can be used as models is not clear. We present two apparently opposed perspectives on this issue: simulation models as "emergent computational thought experiments" and simulation models as realistic simulacra. Through analysis the role that armchair thought experiments play in science, we develop a role for simulation models as opaque thought experiments, that is, thought experiments in which the consequences follow from the premises, but in a non-obvious manner which must be revealed through systematic enquiry. Like their better-known transparent cousins, opaque thought experiments, when understood, result in new insights and conceptual reorganisations. These may stress the current theoretical position of the thought experimenter and engender empirical predictions which must be tested in reality. As such, simulation models, like all thought experiments, are tools with which to explore the consequences of a theoretical position.

The Stanford Encyclopedia of Philosophy, 2020